Real-time hand postures recognition using low computational complexity Artificial Neural Networks and Support Vector Machines

被引：6
|
作者：
Bragatto, Ticiano A. C. [1 ]
Ruas, Gabriel S. I. [1 ]
Lamar, Marcus V. [2 ]
机构：
[1] Univ Brasilia, Dept Elect Eng, Brasilia, DF, Brazil
[2] Univ Brasilia, Dept Comp Sci, Brasilia, DF, Brazil
来源：
2008 3RD INTERNATIONAL SYMPOSIUM ON COMMUNICATIONS, CONTROL AND SIGNAL PROCESSING, VOLS 1-3 | 2008年
关键词：
hand posture recognition; Artificial Neural Networks; Support Vector Machines;
D O I：
10.1109/ISCCSP.2008.4537470
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper proposes two main techniques for reduce computational complexity on Artificial Neural Networks, using piecewise linear activation function, and Support Vector Machines built on a probability based binary tree. These methods are compared with well-known classifiers based on the computational complexity, correct rate and time taken to process the required information. The results show that probability based binary tree SVM has an equivalent recognition rate and is faster than ANNs.
引用
收藏
页码：1530 / +
页数：2
相关论文
共 50 条
  • [1] Real-time Video Based Finger Spelling Recognition System Using Low Computational Complexity Artificial Neural Networks
    Bragatto, T. A. C.
    Ruas, G. I. S.
    Lamar, M. V.
    PROCEEDINGS OF THE IEEE INTERNATIONAL TELECOMMUNICATIONS SYMPOSIUM, VOLS 1 AND 2, 2006, : 393 - 397
  • [2] Object recognition in industrial environments using support vector machines and artificial neural networks
    Timothy John Barry
    C. Romesh Nagarajah
    The International Journal of Advanced Manufacturing Technology, 2010, 48 : 815 - 821
  • [3] Object recognition in industrial environments using support vector machines and artificial neural networks
    Barry, Timothy John
    Nagarajah, C. Romesh
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2010, 48 (5-8): : 815 - 821
  • [4] Hand postures recognition system using artificial neural networks implemented in FPGA
    Oniga, Stefan
    Tisan, Alin
    Mic, Daniel
    Buchman, Attila
    Vida-Ratiu, Andrei
    2007 30TH INTERNATIONAL SPRING SEMINAR ON ELECTRONICS TECHNOLOGY, 2007, : 507 - 512
  • [5] Real-time hand motion estimation using EMG signals with support vector machines
    Yoshikawa, Masahiro
    Mikawa, Masahiko
    Tanaka, Kazuyo
    2006 SICE-ICASE INTERNATIONAL JOINT CONFERENCE, VOLS 1-13, 2006, : 4331 - +
  • [6] Real-Time Hand Gesture Recognition Based on Electromyographic Signals and Artificial Neural Networks
    Motoche, Cristhian
    Benalcazar, Marco E.
    ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2018, PT I, 2018, 11139 : 352 - 361
  • [7] Real-Time Robust Automatic Speech Recognition Using Compact Support Vector Machines
    Solera-Urena, Ruben
    Isabel Garcia-Moral, Ana
    Pelaez-Moreno, Carmen
    Martinez-Ramon, Manel
    Diaz-de-Maria, Fernando
    IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, 2012, 20 (04): : 1347 - 1361
  • [8] A Real-Time Hand Posture Recognition System Using Deep Neural Networks
    Tang, Ao
    Lu, Ke
    Wang, Yufei
    Huang, Jie
    Li, Houqiang
    ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY, 2015, 6 (02)
  • [9] Artificial neural networks for real-time optical hand posture recognition using a color-coded glove
    Malric, Francois
    El Saddik, Abdulmotaleb
    Georganas, Nicolas D.
    2008 IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE FOR MEASUREMENT SYSTEMS AND APPLICATIONS, 2008, : 105 - 110
  • [10] Protein fold recognition using neural networks and support vector machines
    Jiang, N
    Wu, WXY
    Mitchell, I
    INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING IDEAL 2005, PROCEEDINGS, 2005, 3578 : 462 - 469
12345